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This paper considers the learning of logical (Boolean) functions with a focus on the generalization on the unseen (GOTU) setting, a strong case of out-of-distribution generalization.
The construction of multivariate polynomials with preassigned zeros
H Michael Möller and Bruno Buchberger · 1982
Earlier work this paper cites.
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Earlier work this paper cites.
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
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